Prediction of Periventricular Leukomalacia in Neonates after Cardiac Surgery Using Machine Learning Algorithms

Abstract

Periventricular leukomalacia (PVL) is brain injury that develops commonly in neonates after cardiac surgery. Earlier identification of patients who are at higher risk for PVL may improve clinicians’ ability to optimize care for these challenging patients. The aim of this study was to apply machine learning algorithms and wavelet analysis to vital sign and laboratory data obtained from neonates immediately after cardiac surgery to predict PVL occurrence. We analyzed physiological data of patients with and without hypoplastic left heart syndrome (HLHS) during the first 12 h after cardiac surgery. Wavelet transform was applied to extract time-frequency information from the data. We ranked the extracted features to select the most discriminative features, and the support vector machine with radial basis function as a kernel was selected as the classifier. The classifier was optimized via three methods: (1) mutual information, (2) modified mutual information considering the reliability of features, and (3) modified mutual information with reliability index and maximizing set’s mutual information. We assessed the accuracy of the classifier at each time point. A total of 71 neonates met the study criteria. The rates of PVL occurrence were 33% for all patients, with 41% in the HLHS group and 25% in the non-HLHS group. The F-score results for HLHS patients and non-HLHS patients were 0.88 and 1.00, respectively. Using maximizing set’s mutual information improved the classifier performance in the all patient groups from 0.69 to 0.81. The novel application of a modified mutual information ranking system with the reliability index in a PVL prediction model provided highly accurate identification. This tool is a promising step for improving the care of neonates who are at higher risk for developing PVL following cardiac surgery.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

References

  1. 1.

    Galli, K. K., Zimmerman, R. A., Jarvik, G. P., Wernovsky, G., Kuypers, M. K., Clancy, R. R., Montenegro, L. M., Mahle, W. T., Newman, M. F., Saunders, A. M., Nicolson, S. C., Spray, T. L., and Gaynor, J. W., Periventricular leukomalacia is common after neonatal cardiac surgery. J Thorac Cardiovasc Surg. 127(3):692–704, 2004.

    Article  PubMed  Google Scholar 

  2. 2.

    Licht, D. J., Shera, D. M., Clancy, R. R., Wernovsky, G., Montenegro, L. M., Nicolson, S. C. et al., Brain maturation is delayed in infants with complex congenital heart defects. J Thorac Cardiovasc Surg 137(3):529–536; discussion 36-7, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Petit, C. J., Rome, J. J., Wernovsky, G., Mason, S. E., Shera, D. M., Nicolson, S. C. et al., Preoperative brain injury in transposition of the great arteries is associated with oxygenation and time to surgery, not balloon atrial septostomy. Circulation. 119(5):709–716, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Licht, D. J., Wang, J., Silvestre, D. W., Nicolson, S. C., Montenegro, L. M., Wernovsky, G. et al., Preoperative cerebral blood flow is diminished in neonates with severe congenital heart defects. J Thorac Cardiovasc Surg. 128(6):841–849, 2004.

    Article  PubMed  Google Scholar 

  5. 5.

    Samanta, B., Bird, G. L., Kuijpers, M., Zimmerman, R. A., Jarvik, G. P., Wernovsky, G. et al., Prediction of periventricular leukomalacia. Part II: Selection of hemodynamic features using computational intelligence. Artif Intell Med 46(3):217–231, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Newburger, J. W., Sleeper, L. A., Bellinger, D. C., Goldberg, C. S., Tabbutt, S., Lu, M. et al., Early developmental outcome in children with hypoplastic left heart syndrome and related anomalies: The single ventricle reconstruction trial. Circulation. 125(17):2081–2091, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Beca, J., Gunn, J. K., Coleman., L., Hope, A., Reed, P. W., Hunt, R. W. et al., New white matter brain injury after infant heart surgery is associated with diagnostic group and the use of circulatory arrest. Circulation. 127(9):971–979, 2013.

  8. 8.

    Somasundaram, K. S., and Alli, P., A machine learning ensemble classifier for early prediction of diabetic retinopathy. J Med Syst 41(12):201, 2017.

    Article  Google Scholar 

  9. 9.

    Alanazi, H. O., Abdullah, A. H., and Qureshi, K. N., A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J Med Syst. 41(4):69, 2017.

    Article  PubMed  Google Scholar 

  10. 10.

    Wiens, J., and Guttag, J. V., Patient-specific ventricular beat classification without patient-specific expert knowledge: A transfer learning approach. Conference proceedings: IEEE Engineering in Medicine and Biology Society Annual Conference. 2011:5876–5879, 2011.

    Google Scholar 

  11. 11.

    Theodoridis, S., and Koutroumbas, K., Pattern recognition. 4th edition. Burlington, MA: Elsevier, 2008.

    Google Scholar 

  12. 12.

    Jalali, A., Bender, D., Rehman, M., Nadkanri, V., and Nataraj, C., Advanced analytics for outcome prediction in intensive care units. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2016:2520–2524, 2016.

    Google Scholar 

  13. 13.

    Nakashima, H., Tetreault, L., Kato, S., Kryshtalskyj, M. T., Nagoshi, N., Nouri, A., et al. Prediction of outcome following surgical treatment of cervical myelopathy based on features of ossification of the posterior longitudinal ligament: a systematic review. JBJS reviews. 5(2), 2017.

  14. 14.

    Herold, J., Schroeder, R., Nasticzky, F., Baier, V., Mix, A., Huebner, T. et al., Diagnosing aortic valve stenosis by correlation analysis of wavelet filtered heart sounds. Med Biol Eng Comput. 43(4):451–456, 2005.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Acharya, U. R., Joseph, K. P., Kannathal, N., Lim, C. M., and Suri, J. S., Heart rate variability: A review. Med Biol Eng Comput. 44(12):1031–1051, 2006.

    Article  Google Scholar 

  16. 16.

    Bozhokin, S. V., Continuous wavelet transform and exactly solvable model of nonstationary signals. Technical Physics. 57(7):900–906, 2012.

    CAS  Article  Google Scholar 

  17. 17.

    Ghorbanian, P., Devilbiss, D. M., Verma, A., Bernstein, A., Hess, T., Simon, A. J. et al., Identification of resting and active state EEG features of Alzheimer's disease using discrete wavelet transform. Ann Biomed Eng. 41(6):1243–1257, 2013.

    Article  PubMed  Google Scholar 

  18. 18.

    Hwa, R. C., Fluctuation index as a measure of heartbeat irregularity. Nonlinear Phenom Complex Syst. 3(1):93–98, 2008.

    Google Scholar 

  19. 19.

    Liu, Y., Zhou, W., Yuan, Q., and Chen, S., Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Transactions on Neural Systems and Rzehabilitation Engineering 20(6):749–755, 2012.

    Article  Google Scholar 

  20. 20.

    Peng, H., Long, F., and Ding, C., Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8):1226–1238, 2005.

    Article  PubMed  Google Scholar 

  21. 21.

    Sheikholeslami, N., and Stashuk, D., Supervised mutual-information based feature selection for motor unit action potential classification. Med Biol Eng Comput. 35(6):661–670, 1997.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Tourassi, G. D., Frederick, E. D., Markey, M. K., and Floyd, Jr., C. E., Application of the mutual information criterion for feature selection in computer-aided diagnosis. Med Phys. 28(12):2394–2402, 2001.

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., and Tarassenko, L., Signal-quality indices for the electrocardiogram and photoplethysmogram: Derivation and applications to wireless monitoring. IEEE J Biomed Health Inform. 19(3):832–838, 2015.

    PubMed  Google Scholar 

  24. 24.

    Kappaganthu, K., and Nataraj, C., Feature selection for fault detection in rolling element bearings using mutual information. J Vib Acoustics. 133(6):061001–061011, 2011.

    Article  Google Scholar 

  25. 25.

    Scholkopf, B., Smola, A. J., Williamson, R. C., and Bartlett, P. L., New support vector algorithms. Neural Comput. 12(5):1207–1245, 2000.

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Rhinelander, J., and Liu, X. P., Stochastic subset selection for learning with kernel machines. IEEE Trans Syst Man Cybern B Cybern 42(3):616–626, 2012.

    Article  PubMed  Google Scholar 

  27. 27.

    Oyang, Y. J., Hwang, S. C., Ou, Y. Y., Chen, C. Y., and Chen, Z. W., Data classification with radial basis function networks based on a novel kernel density estimation algorithm. IEEE Trans Neural Netw. 16(1):225–236, 2005.

    Article  PubMed  Google Scholar 

  28. 28.

    Rajapakse, J. C., and Mundra, P. A., Multiclass gene selection using Pareto-fronts. IEEE/ACM Trans Comput Biol Bioinform. 10(1):87–97, 2013.

    Article  PubMed  Google Scholar 

  29. 29.

    Licht, D. J., Wang, J., Silvestre, D. W., Nicolson, S. C., Montenegro, L. M., Wernovsky, G. et al., Preoperative cerebral blood flow is diminished in neonates with severe congenital heart defects. J Thorac Cardiovasc Surg. 128(6):841–849, 2004.

    Article  PubMed  Google Scholar 

  30. 30.

    Samanta, B., Bird, G. L., Kuijpers, M., Zimmerman, R. A., Jarvik, G. P., Wernovsky, G., Clancy, R. R., Licht, D. J., Gaynor, J. W., and Nataraj, C., Prediction of periventricular leukomalacia. Part I: Selection of hemodynamic features using logistic regression and decision tree algorithms. Artif Intell Med 46(3):201–215, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The research reported in this paper was supported by a grant from the National Institutes of Health (No. 1 R01 NS 72338 01A1).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ali Jalali.

Ethics declarations

Conflict of interest

Ali Jalali, PhD declares that he has no conflict of interest. Allan F. Simpao, MD, MBI declares that he has no conflict of interest. Jorge A. Galvez, MD declares that he has no conflict of interest. Daniel J. Licht, MD declares that he has no conflict of interest. C. Nataraj, PhD declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

This article is part of the Topical Collection on Image & Signal Processing

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jalali, A., Simpao, A.F., Gálvez, J.A. et al. Prediction of Periventricular Leukomalacia in Neonates after Cardiac Surgery Using Machine Learning Algorithms. J Med Syst 42, 177 (2018). https://doi.org/10.1007/s10916-018-1029-z

Download citation

Keywords

  • Machine learning
  • Leukomalacia, periventricular
  • Heart defects, congenital
  • Decision support systems, clinical
  • Support vector machine
  • Wavelet analysis